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Set AI project data access by defining what people and automated processes need for their assigned work, limiting access to that need, and controlling where data can move. Start with the project’s purpose, data inventory, users, service identities, and data flows; then document permissions, protect privileged accounts, monitor access, and review or revoke permissions as the project changes. Authentication, authorization, and data-flow controls are separate safeguards: a successful sign-in does not by itself grant the right to view a dataset or export it.
Start with the project, data, and people
Before configuring a cloud workspace, model endpoint, database, or collaboration tool, establish what the project does and what information it handles. Access rules for a prototype using public material may differ substantially from rules for a production system using personal, confidential, regulated, or third-party data.
- Purpose and lifecycle: Record the intended AI use and whether the work is in design, development, evaluation, deployment, or operation. Note who may be affected by the system and what consequences misuse or exposure could have.
- Data inventory: Identify datasets and other project components, their owners or sources, sensitivity, permitted uses, and applicable legal, contractual, privacy, or security restrictions. Include prompts, retrieval indexes, logs, evaluation sets, and outputs when they may contain protected information.
- Identities: List human users and non-human identities, such as jobs, service accounts, applications, and integrations. A process that reads a dataset or sends it to a model needs an explicit access decision too.
- Data flows: Map where data comes from, where it is stored and processed, and where it can go next—including exports, hosted models, plugins, retrieval services, and vendors.
NIST’s voluntary AI Risk Management Framework (AI RMF), released January 26, 2023, organizes lifecycle risk work into Govern, Map, Measure, and Manage. NIST’s current AI RMF page says the framework is being revised, and the companion Playbook is to be updated after that revision. Treat these as risk-management resources, not a universal compliance checklist.
Write permissions around duties and data sensitivity
Define the access model before assigning permissions in individual tools. Use roles or attributes that reflect actual responsibilities and need-to-know, rather than granting access because a person belongs to the project. For each role or service identity, specify the data it may access, the actions it may take, and any purpose, time, or environment limits.
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| Access decision | Questions to answer |
|---|---|
| Identity | Which individual, role, application, or process needs access? Can actions be attributed to a specific identity rather than a shared account? |
| Dataset or component | Which specific source, table, file store, model artifact, index, or environment is required? |
| Action | Does the task require viewing, querying, changing, exporting, sharing, or administering? Do not bundle these into one broad permission if the system can separate them. |
| Purpose and limits | What work justifies access? Should it expire, be limited to a particular project stage, or be available only from an approved environment? |
| Approval and accountability | Who approves sensitive access, and what record shows why it was granted and who used it? |
Where the architecture permits, separate permissions by project stage, dataset sensitivity, and action. A developer may need a controlled development copy but not production records; an evaluator may need a test set but not permission to export the source data. Avoid broad shared accounts where individual accountability matters.
NIST SP 800-171 Revision 3 states: “Allow only authorized system access for users (or processes acting on behalf of users) that is necessary to accomplish assigned organizational tasks.” This least-privilege requirement is part of a standard specifically for protecting controlled unclassified information (CUI) in nonfederal systems and organizations; it should not be presented as a formal requirement for every AI project.
Keep authentication, authorization, and data movement distinct
These controls solve different problems and should be planned separately:
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- Authentication establishes which identity is signing in and, depending on the method, how strongly that identity is verified.
- Authorization determines what that identity is allowed to access or do, such as querying a dataset or changing a model configuration.
- Information-flow controls govern where data may move, such as whether it can be exported, sent to an external model, or transferred between systems or security domains.
Set rules for exports, external connections, and cross-system transfers according to data sensitivity and policy. A user who may view a record does not necessarily need permission to download it, and an approved application connection does not make every destination appropriate. Apply the same scrutiny to automated jobs and integrations as to people.
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For personally sensitive data in training sets or production, document the permitted purpose, authorization, access type, and duration. Align collection, use, management, and disclosure with the organization’s privacy and data-governance policies. Consider monitoring production queries for patterns that could isolate individual personal records. De-identification alone does not establish that every later use or release is safe.
Decide which data belongs in each project stage and whether developers, evaluators, operators, and support staff need the same access. Where feasible, use less-sensitive or appropriately controlled data for tasks that do not require direct access to sensitive records. Record exceptions and the rationale for them.
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Limit privileged access and strengthen sign-in
Choose authentication assurance in proportion to the impact of unauthorized access, while accounting for privacy, usability, and the users’ context. Keep administrative accounts and privileged functions limited to appropriate roles; use ordinary accounts for routine work and log privileged actions.
NIST SP 800-63-4, published in 2025, provides digital identity guidance, including phishing-resistant authentication options at higher assurance levels and hardware cryptographic authenticators. A FIDO2 security key can strengthen sign-in to systems that hold project data, but it does not grant or restrict data-level permissions. The authorization rules still need to decide which records and operations the authenticated identity may access.
Assess third-party AI services before connecting data
A hosted model, plugin, retrieval service, or other provider creates a boundary across which project data may travel. Before connecting it, establish:
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- What data the service receives, including prompts, uploaded files, retrieved passages, and logs.
- What the provider’s current contract and technical documentation permit regarding access, retention, use, and disclosure.
- Where data is processed or transferred and which provider personnel or subprocessors may access it.
- How access is controlled, incidents are handled, and material service or terms changes are communicated.
- Whether the connection can be limited to the minimum data and operations needed, and how it can be disabled or revoked.
NIST’s Generative AI Profile (AI 600-1), published July 26, 2024, identifies privacy and information-security risks associated with generative AI and describes due diligence, service-level agreements, and assurance reports as possible risk-management inputs. These are prompts for assessment, not proof that any particular provider meets a requirement; check the provider’s current terms and controls for the service and configuration in use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review permissions, test controls, and keep evidence
Set a documented permission-review frequency based on the data’s sensitivity, project risk, applicable obligations, and how quickly access needs change. NIST SP 800-171 Revision 3 leaves the frequency of periodic reviews organization-defined; there is no single interval established here for every AI project.
Review access sooner when a person changes roles, a project moves to another lifecycle stage, a dataset or integration is added, or a provider changes. Confirm each permission still matches current work, correct excessive access, and remove access that is no longer needed. Test that restrictions work in practice and monitor security-relevant access and unexpected data movement.
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Retain enough evidence to explain and operate the control model: the data inventory and flow map, role and permission definitions, approvals, review records, relevant system logs, third-party assessments, and exceptions. Record why sensitive access is necessary and who accepted any residual risk. Revisit the model when the data, model, intended use, staff, or providers change.
Understand which guidance applies
NIST’s AI RMF and Playbook are voluntary resources. SP 800-171 Revision 3 addresses CUI in nonfederal systems and organizations, while SP 800-63-4 addresses digital identity; neither alone determines every organization’s obligations. Which laws, sector rules, contracts, or internal policies apply depends on the jurisdiction, data, organization, and project. Identify those facts and involve appropriate legal, privacy, and security teams rather than treating a general guide as compliance advice.
AI security guidance is also developing. NIST describes areas of machine-learning attack coverage that remain unresolved and is developing AI security control overlays. Controls should therefore be revisited as the project and relevant guidance evolve.
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